IEICE TRANSACTIONS on Information
Joint Tracking of Performance Model Parameters and System Behavior Using a Multiple-Model Kalman Filter
Summary :
Online resource management of a software system can take advantage of a performance model to predict the effect of proposed changes. However, the prediction accuracy may degrade if the performance model does not adapt to the changes in the system. This work considers the problem of using Kalman filters to track changes in both performance model parameters and system behavior. We propose a method based on the multiple-model Kalman filter. The method runs a set of Kalman filters, each of which models different system behavior, and adaptively fuses the output of those filters for overall estimates. We conducted case studies to demonstrate how to use the method to track changes in various system behaviors: performance modeling, process modeling, and measurement noise. The experiments show that the method can detect changes in system behavior promptly and significantly improve the tracking and prediction accuracy over the single-model Kalman filter. The influence of model design parameters and mode-model mismatch is evaluated. The results support the usefulness of the multiple-model Kalman filter for tracking performance model parameters in systems with time-varying behavior.
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.6 pp.1309-1322
- Publication Date
- 2013/06/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.1309
- Type of Manuscript
- PAPER
- Category
- Software Engineering
Authors
Zhen ZHANG
Computer College of Zhejiang University
Shanping LI
Computer College of Zhejiang University
Junzan ZHOU
Computer College of Zhejiang University
Keyword
Latest Issue
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Zhen ZHANG, Shanping LI, Junzan ZHOU, "Joint Tracking of Performance Model Parameters and System Behavior Using a Multiple-Model Kalman Filter" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 6, pp. 1309-1322, June 2013, doi: 10.1587/transinf.E96.D.1309.
Abstract: Online resource management of a software system can take advantage of a performance model to predict the effect of proposed changes. However, the prediction accuracy may degrade if the performance model does not adapt to the changes in the system. This work considers the problem of using Kalman filters to track changes in both performance model parameters and system behavior. We propose a method based on the multiple-model Kalman filter. The method runs a set of Kalman filters, each of which models different system behavior, and adaptively fuses the output of those filters for overall estimates. We conducted case studies to demonstrate how to use the method to track changes in various system behaviors: performance modeling, process modeling, and measurement noise. The experiments show that the method can detect changes in system behavior promptly and significantly improve the tracking and prediction accuracy over the single-model Kalman filter. The influence of model design parameters and mode-model mismatch is evaluated. The results support the usefulness of the multiple-model Kalman filter for tracking performance model parameters in systems with time-varying behavior.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.1309/_p
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@ARTICLE{e96-d_6_1309,
author={Zhen ZHANG, Shanping LI, Junzan ZHOU, },
journal={IEICE TRANSACTIONS on Information},
title={Joint Tracking of Performance Model Parameters and System Behavior Using a Multiple-Model Kalman Filter},
year={2013},
volume={E96-D},
number={6},
pages={1309-1322},
abstract={Online resource management of a software system can take advantage of a performance model to predict the effect of proposed changes. However, the prediction accuracy may degrade if the performance model does not adapt to the changes in the system. This work considers the problem of using Kalman filters to track changes in both performance model parameters and system behavior. We propose a method based on the multiple-model Kalman filter. The method runs a set of Kalman filters, each of which models different system behavior, and adaptively fuses the output of those filters for overall estimates. We conducted case studies to demonstrate how to use the method to track changes in various system behaviors: performance modeling, process modeling, and measurement noise. The experiments show that the method can detect changes in system behavior promptly and significantly improve the tracking and prediction accuracy over the single-model Kalman filter. The influence of model design parameters and mode-model mismatch is evaluated. The results support the usefulness of the multiple-model Kalman filter for tracking performance model parameters in systems with time-varying behavior.},
keywords={},
doi={10.1587/transinf.E96.D.1309},
ISSN={1745-1361},
month={June},}
Copy
TY - JOUR
TI - Joint Tracking of Performance Model Parameters and System Behavior Using a Multiple-Model Kalman Filter
T2 - IEICE TRANSACTIONS on Information
SP - 1309
EP - 1322
AU - Zhen ZHANG
AU - Shanping LI
AU - Junzan ZHOU
PY - 2013
DO - 10.1587/transinf.E96.D.1309
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2013
AB - Online resource management of a software system can take advantage of a performance model to predict the effect of proposed changes. However, the prediction accuracy may degrade if the performance model does not adapt to the changes in the system. This work considers the problem of using Kalman filters to track changes in both performance model parameters and system behavior. We propose a method based on the multiple-model Kalman filter. The method runs a set of Kalman filters, each of which models different system behavior, and adaptively fuses the output of those filters for overall estimates. We conducted case studies to demonstrate how to use the method to track changes in various system behaviors: performance modeling, process modeling, and measurement noise. The experiments show that the method can detect changes in system behavior promptly and significantly improve the tracking and prediction accuracy over the single-model Kalman filter. The influence of model design parameters and mode-model mismatch is evaluated. The results support the usefulness of the multiple-model Kalman filter for tracking performance model parameters in systems with time-varying behavior.
ER -
English
